Cytoscape is an Open Source bioinformatics environment for biological network analysis, visualization, and modeling. It has grown to become a standard resource in academia and industry, due mainly to its timeliness (it was one of the first tools for visualization of biological networks), open development model (it is still oe of few such tools that is open-source), and public plug-in/Apps interface (allowing anyone to add functionality to Cytoscape and attracting many third-party developers and industrial partners). The NIH has funded Cytoscape development since 2004 under the program Continued Development and Maintenance of Software (R01-GM070743). In this competitive renewal, we will improve, maintain, and support Cytoscape along three Specific Aims. First, we will develop new Cytoscape infrastructure for relating networks across conditions, times, species, and a hierarchy of scales. This infrastructure will be used to implement end-user tools for network comparison and differential analysis, for identifying and visualizing hierarchical network structures, and for aligning these hierarchies against references such as the Gene Ontology. Second, we will work to expand the Cytoscape User Experience (UX), with a focus on improved speed of analysis and display, standardizing and automating common workflows, and collaborative data sharing over the web. This work will take advantage of the latest computing technologies including multi-core processors, distributed computing environments / clouds, general purpose GPU computing, and graph databases. Third, we will continue to maintain and disseminate the Cytoscape code base, including bug tracking / fixing; CPU and memory profiling and optimization; and web-based software distribution of stable and development versions. Also, we will promote new App development through social coding paradigms such as GitHub. Cytoscape is an important milepost on the road to developing large-scale circuit diagrams of the cell. Continued support of Cytoscape will allow other laboratories to avoid reinventing the same tools, time that can instead be devoted to more complex analyses or to basic research.